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1. Algorithmic Trading: What Is It and How Does It Work? Algorithmic trading is a type of trading that uses complex algorithms to determine the best time to buy or sell a stock or other financial instrument. Algorithmic trading systems use mathematical models and computer programs to make decisions about when to buy and sell. These systems can scan markets for potential opportunities, monitor news and events, and execute trades according to predetermined rules. Algorithmic trading has become increasingly popular as technology and computing power have advanced, making it possible to execute trades faster and with greater precision. Algorithmic trading can be used to help traders achieve better returns and reduce risk by eliminating emotions from the decision-making process.

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Fast Romano-Wolf Multiple Testing Corrections for fixest 🐺 | R-bloggers

For the final chapter of my dissertation, I had examined the effects of ordinal class rank on the academic achievement of Danish primary school students (following a popular identification strategy introduced in a paper by Murphy and Weinhard). Because of the richness of the Danish register data, I had a large number of potential outcome variables at my disposal, and as a result, I was able to examine literally all the outcomes that the previous literature had covered in individual studies - the effect of rank on achievement, personality, risky behaviour, mental health, parental investment, etc. - in one paper. Figure 1: The Effect of Ordinal Class Rank on quite a few outcome variables But with (too) many outcome variables comes a risk: inflated type 1 error rates, or an increased risk of ‘false positives’. So I was wondering: were all the significant effects I estimated - shown in the figure above - “real”, or was I simply being fooled by randomness? A common way to control the risk of false positive when testing multiple hypothesis is to use methods that control the ‘family-wise’ error rate, i.e. the risk of at least one false positive in a family of S hypotheses. Among such methods, Romano and Wolf’s correction is particularly popular, because it is “uniformly the most powerful”. Without going into too much detail, I’ll just say that if you have a choice between a number of methods that control the family-wise error rate at a desired level \(\alpha\), you might want to choose the “most powerful” one, i.e. the one that has the highest success rate of finding a true effect. Now, there is actually an amazing Stata package for the Romano-Wolf method called rwolf. But this is an R blog, and I’m an R guy … In addition, my regression involved several million rows and some high-dimensional fixed effects, and rwolf quickly showed its limitations. It just wasn’t fast enough! While playing around with the rwolf package, I finally did my due diligence on the method it implements, and after a little background reading, I realized that both the Romano and Wolf method - as well as its main rival, the method proposed by Westfall and Young - are based on the bootstrap! But wait! Had I not just spent a lot of time porting a super-fast bootstrap algorithm from R to Stata? Could I not use Roodman et al’s “fast and wild” cluster bootstrap algorithm for bootstrap-based multiple hypothesis correction? Of course it was inevitable: I ended up writing an R package. Today I am happy to present the first MVP version of the wildwrwolf package! The wildrwolf package You can simply install the package from github or r-universe by typing # install.packages("devtools") devtools::install_github("s3alfisc/wildrwolf") # from r-universe (windows & mac, compiled R > 4.0 required) install.packages('wildrwolf', repos ='https://s3alfisc.r-universe.dev') The Romano Wolf correction in wildrwolf is implemented as a post-estimation commands for multiple estimation objects from the fabulous fixest package. To demonstrate how wildrwolf's main function, rwolf, works, let’s first simulate some data: library(wildrwolf) library(fixest) set.seed(1412) library(wildrwolf) library(fixest) set.seed(1412) N